DESCRIBER

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ADSL.XPT DATA with 254 OBSERVATIONS and 18 VARIABLES

DESCRIBEstatic

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Every time a dataset is created, either for data management proposes or for statistical analyses, it is imperative that the each variable be reviewed. Not only should the evaluation provide summary statistics and graphical displays to detect data errors, it should also present the results in a thorough, but succinct manner. To accomplish this goal, descriptive summaries for each variable should be created according to their characteristics.

The best available option for generating descriptive data set summaries is found in the Hmisc: Harrell Miscellaneous package for the R statistical programming environment. The function describe function determines whether the variable is character, factor, category, binary, discrete numeric, and continuous numeric, and prints a concise statistical summary according to each.

Of note:

  • For a binary variable, the sum (number of 1’s) and mean (proportion of 1’s) are printed

  • For any variable with at least 20 unique values, the 5 lowest and highest values are printed

  • A numeric variable is deemed discrete if it has <= 10 unique values. In this case, quantiles are not printed.

  • A frequency table is printed for any non-binary variable if it has no more than 20 unique values

DETAILS

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OVERVIEW About the describer package // Go back to main page

“If I take it up I must understand every detail,” said he. “Take time to consider. The smallest point may be the most essential.” — Sherlock Holmes The Adventure of the Red Circle

For a couple of decades we have been loyal users of the Hmisc package in general and the describe function in particular as a way to explore data before any analyses. As it is often the case in the R ecosystem, there are numerous ways to accomplish this task (see summarizing data blogs here and here for a dated but yet extensive review). Our love affair with Hmisc::describe originated due to its concise look pre rmarkdown days (Sweave/Latex/PDF) and its linking with SAS formatted data sets (containing labels, formats, special missing). Indeed, in the clinical research industry, SAS formatted data sets (SAS transport .xpt or native .sas7bdat files) remain widely used, while the R language continues to grow in popularity. Dr. Frank Harrell, who developed the Hmisc package, has been from our perspective a luminary as he lays out the possibilities embedded in the R language especially in the clinical research environment.

For some time now we wanted to modernize the aforementioned describe function to provide a modern and interactive interface after years of the static (HTML and/or PDF) report. The datadigest package was an effort to build an interactive data explorer inspired by Hmisc::describe; the package leveraged JavaScript for interactivity, with htmlwidget and Shiny interfaces for use in R. Since the release of datadigest, the R community has continued to deliver increasingly powerful frameworks for interactive displays. Therefore, We took the 2021 RStudio Table Contest as an opportunity to accomplish the goal of building an interactive interface for describe using tools available in R. In particular by using the power of reactable combined with embedded with plotly interactive figures within a modern flexdashboard to generate concise summaries of every variable in a data set with minimal user configuration. In other for other users to utilize such a powerful summary table we went a step further and wrapped our work into the describer package.

For this challenge we selected a CDISC (Clinical Data Interchange Standards Consortium) ADaM (Analysis Data Model) ADSL (Analysis Data Subject Level) subset dataset as an illustration. The ADSL dataset structure is one record per subject and contains variables such as subject-level population flags, planned and actual treatment variables, demographic information, randomization factors, subgrouping variables, and important dates originated from the PHUSE CDISC Pilot replication study.

AUTHORS

  • Agustin Calatroni
  • Becca Krouse
  • Stephanie Lussier

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INSTRUCTIONS

The {describer} package provides an interface for the interactive table

{describer} consists of two main functions:

  • describe_data(): creates a comprehensive tibble of variable metadata using **Hmisc::describe** as the engine

  • describer(): creates an interactive table using **Hmisc::describe** + **reactable**.

Usage:

Install the package from GitHub:

devtools::install_github("agstn/describer")
library(describer)

Create a tibble summary of the dataset using Hmisc::describe as the engine. This will be passed into the describer function next:

dat_descr <- describe_data(data)

Display results using describer(), which creates a reactable display with columns for variable number (NO), type of variable (TYPE), variable name and label (VARIABLE), number observed (OBSERVED), number and percent missing (MISSING), number of unique values (DISTINCT), and an interactive display (INTERACTIVE FIGURE).

For each variable, there is additional dropdown details based on variable type (character, numeric, date), which are viewable by selecting .

describer(dat_descr)
Interactivity:

  • Built-in Interactivity:

    • Search: Search the dataset variables by label

    • Sort: Sort columns of the reactable variables (alphabetically or numerically)

    • Figures: Interactive figures are provided for each dataset variable dependent on variable type. Zoom and hover for more details.

  • Additional Interactivity:

    • Filters: We can add linked filters by leveraging the power of {crosstalk}. Filters can be created for any of the columns of the describe_data by adding crosstalk widgets and specifying a ‘SharedData’ object in the describer() function. In this example, we offer subsetting by variable type and filtering based on % missing in the sidebar.

METRICS